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Erschienen in:
2015 | OriginalPaper | Buchkapitel
Aesthetic Image Classification Based on Multiple Kernel Learning
verfasst von : Ningning Liu, Xin Jin, Hui Lin, De Zhang
Erschienen in: Computer Vision
Verlag: Springer Berlin Heidelberg
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Aesthetic image classification aims at predicting the aesthetic quality of photos automatically, i.e. whether the photo elicits a high or low level of affection in a majority of people. To solve the problem, one challenge is to build features specific to image aesthetic perceptions, and another one is to build effective learning models to bridge the “semantic gap” between the emotion related concepts and the extracted visual features. In this paper, we present an approach for aesthetic image classification based on Multiple Kernel Learning (MKL) method, which seeks for maximizing the classification performance without explicit feature selection steps. The experiments are conducted on a large diverse database built from online photo sharing website, and the results demonstrated the advantages of MKL in terms of feature selection, classification performance, and interpretation, for the aesthetic image classification task.